200 GPS Sites on Taiwan for Atmospheric Research and Education

Heavy precipitation and strong winds associated with severe weather systems, such as typhoons and Mei-Yu fronts, threaten Taiwan and other places around the world every year. They may cause severe damage to agriculture and industry, and serious loss of human life. Over the past 100 years, Taiwan was hit by 3 to 4 typhoons per year. To reduce the damage, it is necessary to accurately forecast the track, intensity, and precipitation associated with the severe weather systems by incorporating mesoscale observations into numerical weather prediction models through advanced data assimilation techniques. Continuous, accurate, all-weather, real-time GPS moisture data can be used to improve mesoscale modeling and data assimilation.

In light of the tremendous potential of the Global Positioning System (GPS) in atmospheric and geodetic research and education, the Central Weather Bureau (CWB) of Taiwan is planing to set up 200 GPS sites over the island. Currently, 16 GPS sites are deployed by CWB. We will present the potential impact of these new data and observation methods on atmospheric research and education. The capability of linking this CWB GPS network to SuomiNet, a NSF supported real-time national GPS network, will also be explored. In addition, the use of GPS in monitoring spatial and temporal revolutions of PW associated with severe weather systems will be presented. The severe weather systems of concern include Mei-Yu fronts in May-June, and Typhoons Zeb and Babs in late October 1998. GPS-observed PW is compared with that measured by radiosondes at the Taipei Weather Station and that predicted by the Penn State-NCAR non-hydrostatic mesoscale model version 5 (MM5). Consistency in the PW time series among GPS and radiosonde observations, and MM5 predictions is observed. Furthermore, it is found that the GPS scheme captures the extremes of the PW amount better than MM5. These results suggest that GPS-sensed PW is reliable, and can be assimilated into NWP models for further analysis to improve forecasts of severe weather systems.